TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics Data
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
Background: Spatial transcriptomics have emerged as a powerful tool in
biomedical research because of its ability to capture both the spatial contexts
and abundance of the complete RNA transcript profile in organs of interest.
However, limitations of the technology such as the relatively low resolution
and comparatively insufficient sequencing depth make it difficult to reliably
extract real biological signals from these data. To alleviate this challenge,
we propose a novel transfer learning framework, referred to as TransST, to
adaptively leverage the cell-labeled information from external sources in
inferring cell-level heterogeneity of a target spatial transcriptomics data.
Results: Applications in several real studies as well as a number of
simulation settings show that our approach significantly improves existing
techniques. For example, in the breast cancer study, TransST successfully
identifies five biologically meaningful cell clusters, including the two
subgroups of cancer in situ and invasive cancer; in addition, only TransST is
able to separate the adipose tissues from the connective issues among all the
studied methods.
Conclusions: In summary, the proposed method TransST is both effective and
robust in identifying cell subclusters and detecting corresponding driving
biomarkers in spatial transcriptomics data.
